Abstract Traditionally, an (n1*n2) image is represented by a vector in the Euclidean space R(n1*n2), thus the spatial relationships between pixels in an image are ignored. In this paper, the images are presented as points in the tensor space Rn1Rn2. Then, a semi-supervised dimensionality reduction algorithm is put forward based on pairwise constraints (must-link and cannot-link)between the images. The data in the reduced space preserve the local structure of the data manifold well. Finally, experimental results on face datasets validate the effectiveness of the proposed algorithm.
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2009, Vol. 22 
